The Role of Hypoxia in 2-Butoxyethanol–Induced Hemangiosarcoma

To understand the molecular mechanisms underlying compound-induced hemangiosarcomas in mice, and therefore, their human relevance, a systems biology approach was undertaken using transcriptomics and Causal Network Modeling from mice treated with 2-butoxyethanol (2-BE). 2-BE is a hemolytic agent that induces hemangiosarcomas in mice. We hypothesized that the hemolysis induced by 2-BE would result in local tissue hypoxia, a well-documented trigger for endothelial cell proliferation leading to hemangiosarcoma. Gene expression data from bone marrow (BM), liver, and spleen of mice exposed to a single dose (4 h) or seven daily doses of 2-BE were used to develop a mechanistic model of hemangiosarcoma. The resulting mechanistic model confirms previous work proposing that 2-BE induces macrophage activation and inflammation in the liver. In addition, the model supports local tissue hypoxia in the liver and spleen, coupled with increased erythropoeitin signaling and erythropoiesis in the spleen and BM, and suppression of mechanisms that contribute to genomic stability, events that could be contributing factors to hemangiosarcoma formation. Finally, an immunohistochemistry method (Hypoxyprobe) demonstrated that tissue hypoxia was present in the spleen and BM. Together, the results of this study identify molecular mechanisms that initiate hemangiosarcoma, a key step in understanding safety concerns that can impact drug decision processes, and identified hypoxia as a possible contributing factor for 2-BE–induced hemangiosarcoma in mice

RNA-Seq Differentiates Tumour and Host mRNA Expression Changes Induced by Treatment of Human Tumour Xenografts with the VEGFR Tyrosine Kinase Inhibitor Cediranib.

Pre-clinical models of tumour biology often rely on propagating human tumour cells in a mouse. In order to gain insight into the alignment of these models to human disease segments or investigate the effects of different therapeutics, approaches such as PCR or array based expression profiling are often employed despite suffering from biased transcript coverage, and a requirement for specialist experimental protocols to separate tumour and host signals. Here, we describe a computational strategy to profile transcript expression in both the tumour and host compartments of pre-clinical xenograft models from the same RNA sample using RNA-Seq. Key to this strategy is a species-specific mapping approach that removes the need for manipulation of the RNA population, customised sequencing protocols, or prior knowledge of the species component ratio. The method demonstrates comparable performance to species-specific RT-qPCR and a standard microarray platform, and allowed us to quantify gene expression changes in both the tumour and host tissue following treatment with cediranib, a potent vascular endothelial growth factor receptor tyrosine kinase inhibitor, including the reduction of multiple murine transcripts associated with endothelium or vessels, and an increase in genes associated with the inflammatory response in response to cediranib. In the human compartment, we observed a robust induction of hypoxia genes and a reduction in cell cycle associated transcripts. In conclusion, the study establishes that RNA-Seq can be applied to pre-clinical models to gain deeper understanding of model characteristics and compound mechanism of action, and to identify both tumour and host biomarkers

Electronically subtracting expression patterns from a mixed cell population

Motivation: Biological samples frequently contain multiple cell-types that each can play a crucial role in the development and/or regulation of adjacent cells or tissues. The search for biomarkers, or expression patterns of, one cell-type in those samples can be a complex and time-consuming process. Ordinarily, extensive laboratory bench work must be performed to separate the mixed cell population into its subcomponents, such that each can be accurately characterized. Results: We have developed a methodology to electronically subtract gene expression in one or more components of a mixed cell population from a mixture, to reveal the expression patterns of other minor or difficult to isolate components. Examination of simulated data indicates that this procedure can reliably determine the expression patterns in cell-types that contribute as little as 5% of the total expression in a mixed cell population. We re-analyzed microarray expression data from the viral infection of macrophages and from the T-cells of wild type and Foxp3 deletion mice. Using our subtraction methodology, we were able to substantially improve the identification of genes involved in processes of subcomponent portions of these samples. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

A systematic review of artificial intelligence and machine learning applications to inflammatory bowel disease, with practical guidelines for interpretation

Background: Inflammatory bowel disease (IBD) is a gastrointestinal chronic disease with an unpredictable disease course. Computational methods such as machine learning (ML) have the potential to stratify IBD patients for the provision of individualised care. The use of ML methods for IBD was surveyed, with an additional focus on how the field has changed over time. Methods: A systematic review was conducted through a search of MEDLINE and Embase databases, with the search structure (“machine learning” OR “artificial intelligence”) AND (“Crohn* Disease” OR “Ulcerative Colitis” OR “Inflammatory Bowel Disease”), searched 6th May 2021. Exclusion criteria: studies not written in English, no human patient data, publication before 2001, studies that were not peer reviewed, non-autoimmune disease comorbidity research and record types that were not primary research. Results: 78 (of 409) records met the inclusion criteria. Random forest methods were most prevalent, and there was an increase in neural networks, mainly applied to imaging datasets. The main applications of ML to clinical tasks were diagnosis (18/78), disease course (22/78) and disease severity (16/78). The median sample size was 263. Clinical and microbiome-related datasets were most popular. 5% of studies used an external dataset after training and testing for additional model validation.Discussion: Availability of longitudinal and deep phenotyping data could lead to better modelling. ML pipelines considering imbalanced data, and feature selection only on training data will generate more generalisable models. ML models are increasingly being applied to more complex clinical tasks for specific phenotypes, indicating progress towards personalised medicine for IBD

Comparing molecular and computational approaches for detecting viral integration of AAV gene therapy constructs

Many current gene therapy targets use recombinant adeno-associated virus (AAV). The majority of delivered AAV therapeutics persist as episomes, separate from host DNA, yet some viral DNA can integrate into host DNA in different proportions and at genomic locations. The potential for viral integration leading to oncogenic transformation has led regulatory agencies to require investigation into AAV integration events following gene therapy in preclinical species. In the present study, tissues were collected from cynomolgus monkeys and mice 6 and 8 weeks, respectively, following administration of an AAV vector delivering transgene cargo. We compared three different next-generation sequencing approaches (shearing extension primer tag selection ligation-mediated PCR, targeted enrichment sequencing [TES], and whole-genome sequencing) to contrast the specificity, scope, and frequency of integration detected by each method. All three methods detected dose-dependent insertions with a limited number of hotspots and expanded clones. While the functional outcome was similar for all three methods, TES was the most cost-effective and comprehensive method of detecting viral integration. Our findings aim to inform the direction of molecular efforts to ensure a thorough hazard assessment of AAV viral integration in our preclinical gene therapy studies

Table S1

Taqman primer and probe sequences for RT-qPCR mRNA integrity assay targeting Actb (NM_007393.5

Table S6

Summary of total gene counts and quantity of unique genes identified prior to count normalizatio